(a) Technical Field of the Invention
The present invention relates to the field of target tracking and more generally to a method employing improved algorithms, which achieve excellent tracking performance for a high-g maneuvering target.
(b) Description of the Prior Art
Target tracking is a critical component in multi-platform Multi-sensor Data Fusion(MSDF) for tactical situation awareness of the dynamic battlefield and for threat assessment. For most airborne fire control radar in fighters, the simple Kalman filter is widely used as the target tracking technique. However target maneuvers are highly unpredictable and can range from 2 g to more than 9 g. The Kalman filter will have to compromise between accuracy and track loss. Both of which are detrimental to weapon engagement.
The Kalman filter is the conventional and most widely used state estimator for providing optimal tracking performance based on the critical assumption that a single state model is required. In the practical multi-target tracking scenario, the targets being tracked will at times undergo maneuvers. which cannot be modeled well for the Kalman filter. To achieve superior performance while tracking maneuvering targets, it is necessary that the estimator allows for the motion of a target to be described by different models with different state equations in different time intervals. A hybrid system can match the above target model well and systems with Markovian switching parameters are typical examples of the hybrid systems. This has been disclosed in “MULTITARGET-MULTISENSORS TRACKING: PRINCIPLES AND TECHNIQUES”, Norwood, Mass.: Artech House, 1995, by Y. Bar-Shalom and X. R. Li.
However, the estimator for hybrid systems has exponentially increasing complexity. The optimal approach to estimate the target state requires that every possible model sequence from the initial through the most recent observation to be considered. Thus for M models and N observations, there are MN hypotheses to consider. This is not practical and hence efficient management of the multiple hypotheses is critical to limiting computational requirements while maintaining performance. Many techniques such as generalized pseudo Bayesian algorithm (e.g. GPB1 and GPB2) have been employed to reduce the number of hypotheses and most search in the literature indicate that the two model IMM algorithm is the preferred technique for real-time tracking of maneuvering targets, considering the performance and computational requirements. With the IMM estimator, an explicit provision is made for the target motion to automatically “switch” from one motion model to another in a probabilistic manner, thereby achieving adaptive bandwidth adjustment required by a maneuvering target tracking filter.
Compared with IMM algorithm, another effective maneuvering target track algorithm, the Interacting Acceleration Compensation algorithm, was proposed by Watson and Blair in “INTERACTING ACCELERATION COMPENSATION ALGORITHM FOR TRACKING MANEUVERING TARGETS”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, No. 2, Jul. 1995. The simulation results from “INTERACTING ACCELERATION COMPENSATION ALGORITHM FOR TRACKING MANEUVERING TARGETS” showed that the IAC algorithm saves about 50% computation cost in comparison to IMM but with slight degradation in tracking performance. The algorithm is viewed as a two-stage estimator having two acceleration models: the zero acceleration of the constant velocity model and a constant acceleration model. It combines the concept of the IMM for two motion models into the framework of the two-stage estimator.
In the practical implementation, the real-time performances of the above two algorithms are not satisfactory enough, especially in high-g maneuvering air target tracking. In view of these drawbacks, it is a principle object of the present invention to provide a maneuvering target tracking method via modifying the Interacting Multiple Model (IMM) and the Interacting Acceleration Compensation (IAC) algorithms to achieve a better tracking performance.